Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. The wide acceptance can be attributed to its robustness to high dimensionality problem. However, when the high-dimensional data is a sparse one, RF procedures are ineffi...
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| Format: | Thèse |
| Langue: | anglais anglais anglais |
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2018
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| Accès en ligne: | http://eprints.uthm.edu.my/326/ |
| Abstract | Abstract here |